Canada's vast oil sand reserve consists of an estimated 173 billion barrels of oil, ranking Canada as the third largest oil reserve in the world. While it is estimated that some 90% of Canada's oil production will be exclusively from the oil sands by 2030, it should be noted that production of oil from oil sands is not simple. Oils sands are a mixture of sand, water, and bitumen. Bitumen is a thick, sticky form of crude oil, so heavy and viscous (thick) that it will not flow unless heated or diluted with lighter hydrocarbons. When near the surface, bitumen is typically extracted by surface mining. However, as shown in FIG. 1, the nature of the oil sands reserves in Canada has only limited application because most bitumen is located greater than 75 meters below the surface. Consequently, in situ extraction methods have become the predominant method of recovery in Canada.
Mineable area 101, only provides access to 19% of the reserves, the other 81% must be produced in situ 102. Open pit mines 103 use excavators 104 to access oil sands 105. Intermediate in situ 106 for oil sands from 75-200 meters may be accessed via primary production or cold heavy oil production with sand. While oil sands greater than 200 meters are typically accessed via in situ thermal recovery techniques. Steam injection 108 produces a thermal chamber 109 for oil recovery 107.
Conventional approaches to recovering heavy oils such as bitumen often focus on lowering the viscosity through the addition of heat. Commonly used in situ extraction thermal recovery techniques include a number of reservoir heating methods, such as steam flooding, cyclic steam stimulation, and Steam Assisted Gravity Drainage (SAGD).
SAGD is the most extensively used technique for in situ recovery of bitumen resources in the Canadian and Venezuelan deposits and other reservoirs containing viscous hydrocarbons. FIG. 2 displays the typical SAGD system. In SAGD, two horizontal wells 204 and 205 are vertically spaced by 4 to 10 meters (m). The production well 204 is located near the bottom of the pay 201 and the steam injection well 205 is located directly above and parallel to the production well. In SAGD, steam generated 202 by a e.g., a Once-Through Steam Generator (OTSG), is injected continuously 207 into the injection well, where it rises in the reservoir and forms a steam chamber. The heat from the steam reduces the oil's viscosity, thus enabling it to flow down under the pull of gravity to the production well and transported to the surface 208 via pumps or lift gas. The produced oil and water 206 is processed 203 to generate oil.
SAGD is of considerable interest in the oil industry because of the vast amount of bitumen that can be produced. The total amount of yet un-extracted crude bitumen in Alberta, Canada alone is estimated to be about 310 billion barrels (50×109 m3), which at a production rate of 4,400,000 barrels per day (700,000 m3/d) would last about 200 years. However, SAGD recovery shortcomings are mostly related to geological aspects of the reservoirs that are not fully understood.
Reservoir simulation studies are increasingly being conducted to improve our understanding of reservoir response to steam injection. As such, modeling of SAGD processes has become imperative to optimizing recovery. FIG. 3 displays a recent literature search for the number of publications related to proxies, surrogates and metamodels in the field of oil and gas. Since 2010, there has been an overwhelming increase in publications, especially those geared towards SAGD modeling.
Butler and Stephens (1981) proposed the first SAGD analytical model, which is based on the one-dimensional conduction heat transfer theory ahead of an advancing front and a nonlinear assumption for the viscosity gradient relationship. This model was able to adequately mimic the SAGD process without using geomechanics as part of the physical assumptions. However, as operators are gaining experience with the SAGD process, it is becoming clear that oil sands are anything but homogenous and have tremendous variations in key geological and reservoir properties. For instance, the geomechanical behavior is based on a composition of oil sand grains, which can be densely packed and have an interlocked structure. There is much difficulty in including this information in reservoir modeling.
Many flow simulators are available for predicting SAGD performance and support reservoir management decisions, but are CPU intensive simulations based on finite difference models. Thus, building models, especially 3D models, for e.g. thermal simulation is significantly more complex than for conventional simulations, requiring more computing power, more iteration, and more memory. This is especially true in SAGD where there may be several well pair scenarios. Simulations may take several days and even weeks to complete a particular configuration. Furthermore, the estimation of prediction uncertainties based on reservoir models with long run times is often impractical due to limited statistical data generated from direct full field reservoir simulation runs.
As such, there has been a shift to using “surrogate” or “proxy” models (used interchangeably herein) to perform a full field assessment. These models mimic the behavior of the full simulation model, but are less expensive and less CPU demanding. Surrogate models are usually statistical or mathematical models that approximate an existing system and are considered a reduced version of the simulation model. Generally, these models are built using estimation algorithms to process the response of the system, but they can capture key performance indicators as a function of reservoir uncertainty.
Most solutions to increase turn around time through use of surrogates have relied on variants or extensions to the original analytical model proposed by Butler. Other efforts have tried to explore strictly data driven techniques, such as neural networks or polynomial regressions, instead of analytical models. The main shortcoming of these approaches is the use of strongly restrictive physical assumptions (e.g., homogeneity, 2D solutions, no-interwell interaction) as in the case of Butler's model, or the lack of physical structure, as in the case of data driven techniques.
Thus, there exist a need to develop physics-sound surrogate models that could be proactively used in field operations and lead to more reliable decisions. Ideally, the models would offer faster modeling with full field assessment capabilities, preferably with fast numerical and analytical surrogates as well as allowing for the possibility to inspect “what-if” production scenarios.